Geographic information processing method and device, equipment and storage medium

Abstract

The invention provides a geographic information processing method and device, equipment and a storage medium, and relates to the technical field of GIS data processing, the method comprises the following steps: obtaining vector geographic information data of multiple formats, the vector geographic information data at least comprising a vector layer containing land class attributes; processing the vector of the vector layer through a data editing processing tool to obtain a target vector layer; obtaining an intersection region obtained after the plurality of target vector layers are subjected to intersection operation, and taking the intersection region as a first result layer; according to the layer attribute of the first result layer, performing classified rendering on the first result layer to obtain a second result layer; and performing map finishing on the second result layer to obtain a target map file. The technical effects of the application are that an efficient and intelligent solution is provided, the requirements of modern geographic information processing are met, and powerful support is provided for spatial decision and analysis of each industry.

Description

Technical Field

[0001] The present application relates to the technical field of GIS data processing, and specifically to a geographic information processing method, device, equipment and storage medium. Background Art

[0002] With the rapid development and widespread application of Geographic Information System (GIS) technology, processing and analyzing complex geospatial data has become an important requirement for all walks of life. However, in practical applications, geographic information data often comes from various sources and formats, and contains a large number of vector layers that require complex processing and analysis. How to efficiently integrate these heterogeneous data and conduct in-depth spatial analysis and visualization has become an important technical challenge facing the current GIS field.

[0003] Existing GIS software usually adopts a step-by-step, multi-tool approach to solve this problem. Users need to first import data in different formats into the system, and then use various independent tools to perform data editing, spatial analysis, and mapping operations. Although this method can complete basic data processing and analysis tasks, when dealing with a large number of complex vector layers, users need to frequently switch between different functional modules, which increases the difficulty of operation and the risk of errors. Summary of the invention

[0004] The present application provides a geographic information processing method, apparatus, device and storage medium, which are used to provide an efficient and intelligent solution that can meet the needs of modern geographic information processing and provide strong support for spatial decision-making and analysis in various industries.

[0005] In a first aspect, the present application provides a geographic information processing method, which is applied to a desktop geographic information system, and the method includes: obtaining vector geographic information data in multiple formats, and the vector geographic information data includes at least one vector layer containing land attributes; processing the vector of the vector layer through a data editing and processing tool to obtain a target vector layer; obtaining an intersection area obtained after performing an intersection operation on multiple target vector layers, and using the intersection area as a first result layer; classifying and rendering the first result layer according to the layer attributes of the first result layer to obtain a second result layer; performing map finishing on the second result layer to obtain a target map file.

[0006] By adopting the above technical solution, the problem of data source diversity is first solved by obtaining vector geographic information data in multiple formats. Subsequently, the vector layer is processed using data editing and processing tools to obtain the target vector layer, thereby improving data quality and consistency. By performing intersection operations on multiple target vector layers, the first result layer is obtained, thereby realizing complex spatial analysis. The first result layer is classified and rendered to obtain the second result layer, thereby enhancing the visualization of the data. Finally, the second result layer is map-finished to generate the target map file, thereby completing the conversion from raw data to professional map products. This coherent workflow not only improves the efficiency of data processing and analysis, but also ensures data consistency throughout the entire process, ultimately producing high-quality map files that meet the needs of various professional applications.

[0007] Optionally, the processing of the vectors of the vector layer by a data editing and processing tool to obtain a target vector layer includes: simplifying the vectors of the vector layer to obtain a target vector; dividing the target vector into at least two sub-vectors, and adding nodes at the corners of the sub-vectors by a node tool, wherein the corners are positions on the sub-vectors where the curvature is greater than a preset curvature threshold; performing node editing on the sub-vectors after adding the nodes to obtain a target sub-vector; and merging all the target sub-vectors to obtain a target vector layer.

[0008] By adopting the above technical solution, first, the vector layer is simplified, redundant data is reduced, and processing efficiency is improved. Subsequently, the target vector is divided into sub-vectors and nodes are added at the corners. This step enhances the ability to express complex geographic features. By adding nodes at locations where the curvature is greater than the preset threshold, the accurate representation of key geographic features is ensured. The sub-vectors after adding nodes are edited to further optimize the data quality. Finally, all target sub-vectors are merged into the target vector layer, which not only retains the details but also ensures the integrity. This processing method not only improves the accuracy and expressiveness of geographic data, but also lays a solid foundation for subsequent spatial analysis and visualization, making the final generated map more accurate and reliable. The whole process not only ensures the refined processing of data, but also maintains the consistency and efficiency of operations, providing strong support for high-quality geographic information processing.

[0009] Optionally, after taking the intersecting area as the first result layer, the method further includes: performing a topological rule check on the first result layer, marking the topological errors found on the first result layer, the topological rules including no overlapping patches and no seamless patches; and using an editing tool to correct the topological errors marked on the first result layer so that the first result layer does not contain the topological errors.

[0010] By adopting the above technical solution and introducing the topology rule checking and correction steps, the data quality and spatial accuracy of the first result layer are significantly improved. The topology rule checking of the first result layer, especially the two key rules of no overlapping and no gaps, can effectively identify potential errors in spatial data. The topology errors found are directly marked on the first result layer, providing a clear target for subsequent corrections. The identified topology errors are accurately corrected using editing tools to ensure that the first result layer eventually reaches a high-quality state without topology errors. This process not only improves the integrity and continuity of spatial data, but also enhances the reliability and consistency of data. By eliminating common problems such as overlapping and gaps, this method significantly improves the accuracy of spatial analysis and lays a solid foundation for subsequent classification rendering and map finishing. This automated error detection and correction mechanism greatly reduces the need for manual intervention, improves the efficiency of data processing, and ensures the high quality and professionalism of the final map product.

[0011] Optionally, obtaining the intersection area obtained after performing intersection operation on multiple target vector layers and taking the intersection area as the first result layer includes: selecting multiple target vector layers, setting parameters of the intersection operation, the parameters including the path and name of the input layer and the output layer; performing overlay analysis on the multiple target vector layers according to the parameters to obtain the geometric intersection area between the target vector layers; and converting the geometric intersection area into a new vector layer as the first result layer.

[0012] By adopting the above technical solution, by selecting multiple target vector layers and setting the intersection operation parameters, clear guidance and flexibility are provided for subsequent operations. This parameterized setting not only improves the accuracy of the operation, but also enhances the controllability and repeatability of the processing process. Subsequently, an overlay analysis is performed according to the set parameters to accurately identify the geometric intersection areas between the target vector layers. This step effectively reveals the spatial associations between different geographic elements. Finally, the obtained geometric intersection areas are converted into a new vector layer as the first result layer. This conversion process not only retains the complex spatial relationship information, but also converts it into a form that can be further analyzed and visualized. The entire process realizes the efficient conversion from multiple independent layers to a comprehensive result layer, providing a high-quality data foundation for subsequent spatial analysis, classification rendering and map production, and greatly improving the efficiency and accuracy of geographic information processing.

[0013] Optionally, the first result layer is classified and rendered according to the layer properties of the first result layer to obtain a second result layer, including: extracting the rendering properties of multiple spot elements in the first result layer; matching the rendering symbol parameters corresponding to each of the spot elements in a rendering rule library according to the rendering properties; symbolically rendering each of the spot elements according to the rendering symbol parameters corresponding to each of the spot elements to generate rendered spot elements; and constructing all of the rendered spot elements into a result layer after symbolic rendering to obtain a second result layer.

[0014] By adopting the above technical solution, firstly, by extracting the rendering attributes of multiple map elements in the first result layer, a data foundation is laid for the subsequent rendering process. Subsequently, the rendering rule library is used for attribute matching to ensure that the selection of rendering symbol parameters is both in line with professional standards and consistent. This rule-based matching method not only improves the degree of automation of the rendering process, but also ensures the standardization and reliability of the rendering results. Symbolic rendering is performed on each map element according to the matching rendering symbol parameters. This step converts abstract spatial data into intuitive visual expression. Finally, all rendered map elements are integrated into the result layer after symbolic rendering to obtain the second result layer, realizing the complete conversion from data to visual map. The whole process not only improves the efficiency and quality of map production, but also enhances the expressiveness and interpretability of geographic information, providing a high-quality visualization foundation for subsequent map finishing and final product output.

[0015] Optionally, the map finishing of the second result layer to obtain a target map file includes: symbolizing the second result layer according to preset mapping rules to obtain a symbolized result layer; automatically generating annotation information of layer elements on the symbolized result layer according to preset annotation rules to obtain a third result layer; optimizing the layout of the third result layer and adding preset essential map elements to obtain a fourth result layer; exporting the fourth result layer as a target map file, the format of the target map file being a general electronic map format of vector or raster.

[0016] By adopting the above technical solution, the second result layer is symbolized according to the preset mapping rules, which ensures the standardization and consistency of map symbols and improves the professional level of the map. Subsequently, the annotation information of the layer elements is automatically generated based on the preset annotation rules to obtain the third result layer. This step not only improves the information richness of the map, but also ensures the accuracy and readability of the annotation. The layout of the third result layer is optimized and the necessary elements of the map are added to form the fourth result layer, which further enhances the integrity and beauty of the map. Finally, the fourth result layer is exported as a target map file in a universal electronic map format, which not only ensures the versatility of the results, but also meets the needs of different application scenarios. The whole process realizes the automated conversion from raw data to high-quality map products, significantly improves the mapping efficiency and the quality of the results, and ensures that the map meets professional standards and user needs. This automated map finishing method not only reduces manual intervention, but also ensures the consistency and repeatability of map production, and provides high-quality visualization support for various geographic information applications.

[0017] Optionally, before acquiring vector geographic information data in multiple formats, the method further includes: receiving a data acquisition instruction input by a user, the data acquisition instruction including location information of a data source; acquiring the vector geographic information data in the data source through file import or a network data interface according to the location information; performing coordinate conversion and data format conversion on the acquired vector geographic information data, converting the acquired vector geographic information data into a data format compatible with the desktop geographic information system and vector geographic information data in a unified coordinate system, and using the converted vector geographic information data as the vector geographic information data in the multiple formats.

[0018] By adopting the above technical solution, by receiving the data acquisition instructions input by the user, including the location information of the data source, the user customization of the data acquisition process is realized, and the adaptability of the system is enhanced. According to the location information, the vector geographic information data is obtained through file import or network data interface, which expands the diversity of data sources and enables the system to process data from different sources and formats. Subsequently, the acquired data is subjected to coordinate conversion and data format conversion, and converted into a data format and a unified coordinate system compatible with the desktop geographic information system. This step solves the problem of data heterogeneity and ensures the consistency and accuracy of subsequent processing. Through these preprocessing steps, the system can efficiently integrate vector geographic information data in various formats, providing a standardized and high-quality data foundation for subsequent data editing, spatial analysis and map production. This intelligent data acquisition and preprocessing method not only improves the efficiency of data processing, but also enhances the data compatibility and scalability of the system, making the entire geographic information processing process smoother and more reliable.

[0019] In a second aspect, the present application provides a geographic information processing device, which includes: a first acquisition module, an editing module, a second acquisition module, a rendering module and an output module; wherein the first acquisition module is used to acquire vector geographic information data in multiple formats, and the vector geographic information data includes at least one vector layer containing land class attributes; the editing module is used to process the vector of the vector layer through a data editing and processing tool to obtain a target vector layer; the second acquisition module is used to acquire an intersection area obtained after performing an intersection operation on multiple target vector layers, and use the intersection area as a first result layer; the rendering module is used to classify and render the first result layer according to the layer attributes of the first result layer to obtain a second result layer; the output module is used to perform map finishing on the second result layer to obtain a target map file.

[0020] In the third aspect, the present application provides an electronic device, adopting the following technical solution: including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any one of the above-mentioned geographic information processing methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned geographic information processing methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. By acquiring vector geographic information data in multiple formats, the problem of data source diversity is solved. Subsequently, the vector layer is processed using data editing and processing tools to obtain the target vector layer, thereby improving data quality and consistency. By performing intersection operations on multiple target vector layers, the first result layer is obtained, thereby achieving complex spatial analysis. The first result layer is classified and rendered to obtain the second result layer, thereby enhancing the visualization of the data. Finally, the second result layer is map-finished to generate the target map file, thereby completing the conversion from raw data to professional map products. This coherent workflow not only improves the efficiency of data processing and analysis, but also ensures data consistency throughout the entire process, ultimately producing high-quality map files that meet the needs of various professional applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a geographic information processing method provided in an embodiment of the present application; Figure 2is a schematic diagram of the structure of a geographic information processing device provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] The desktop geographic information system in this application is specifically the LStarGIS desktop geographic information system, which is a software application specially designed for processing, analyzing and visualizing geospatial data.

[0028] LStarGIS Desktop Geographic Information System is a professional GIS software with comprehensive functions and powerful performance. It integrates the management, analysis and visualization of geographic data in a desktop application, providing users with a complete geographic information processing platform. This system makes the processing and analysis of geospatial data more efficient and convenient, and provides powerful tool support for professionals in the fields of urban planning, resource management, environmental monitoring, etc.

[0029] Figure 1 is a flowchart of a geographic information processing method provided by an embodiment of the present application. The present application discloses a geographic information processing method, such as Figure 1 As shown, the method includes S101-S105.

[0030] S101, obtaining vector geographic information data in multiple formats, where the vector geographic information data at least includes a vector layer containing land attributes.

[0031] Specifically, in the field of GIS, vector data is a data format that uses geometric figures such as points, lines, and surfaces to represent geographic elements. Vector geographic information data in multiple formats means that these vector data may be stored in different file formats or data structures.

[0032] A vector layer is a collection of vector features with the same attribute structure. Land attributes are attributes that describe land use or land cover types.

[0033] In one example, the system first receives a data acquisition instruction input by the user, which contains the location information of the data source. This design allows users to flexibly specify the data source, improving the applicability of the system. Based on the location information provided by the user, the system can obtain vector geographic information data from the data source through file import or network data interface. This dual acquisition method ensures that the system can adapt to different data storage and transmission scenarios, and enhances the flexibility and compatibility of data acquisition.

[0034] After acquiring the data, the system will perform necessary preprocessing on these vector geographic information data. This includes coordinate conversion and data format conversion, the purpose of which is to convert the acquired vector geographic information data into a data format compatible with the desktop geographic information system and unify them under the same coordinate system. Through this conversion, the system can process geographic data from different sources and in different formats, greatly improving the system's data compatibility and processing capabilities.

[0035] During data acquisition and conversion, the system pays special attention to vector layers containing land class attributes. Such layers are particularly important for subsequent geographic information analysis, because land class information is the basis of many spatial analysis and mapping processes. By ensuring that at least one such layer is included, the system lays the foundation for subsequent classification rendering and thematic mapping.

[0036] S102, processing the vectors of the vector layer by using a data editing and processing tool to obtain a target vector layer.

[0037] In one example, the system first simplifies the vectors of the vector layer. This step is necessary because the original data may contain too many redundant nodes, which increases the data volume and may slow down the processing speed. The simplification process uses the Douglas-Peucker algorithm, which can effectively reduce the number of nodes while maintaining the overall shape of the graph. The system will simplify according to a preset tolerance value (for example, 0.1 meters) to ensure that the deviation between the simplified graph and the original graph does not exceed this threshold. Doing so can not only reduce the amount of data and increase the subsequent processing speed, but also maintain the basic shape and topological relationship of the graph.

[0038] After simplification, the system splits the target vector into at least two sub-vectors. The purpose of this segmentation is to control and edit complex graphics more finely. The selection of segmentation points is based on the geometric characteristics of the graphics, such as the midpoint of the length or area, or the characteristic points of the graphics (such as the convex points of polygons). After segmentation, the system uses the node tool to add nodes at the corners of the sub-vectors. The corners here are defined as the positions on the sub-vectors where the curvature is greater than the preset curvature threshold, for example, the threshold can be set to 30 degrees. The purpose of adding nodes is to provide more editing control points while maintaining the overall shape of the graphics, making subsequent editing more precise and flexible.

[0039] Next, the system performs node editing on the sub-vectors after adding nodes. This step allows the operator to fine-tune the graph according to actual needs. For example, nodes can be moved to make the boundaries more consistent with the actual contours of the objects, or unnecessary nodes can be deleted to simplify the graph. This fine editing can significantly improve the quality and accuracy of the data, especially when dealing with high-precision maps or conducting detailed spatial analysis.

[0040] Finally, the system merges all the edited target sub-vectors to obtain the final target vector layer. During the merging process, the system will check and maintain the topological relationship of the layers to ensure that the merged layers remain continuous and consistent in space.

[0041] Based on the above embodiment, as an optional implementation, in S102, the vector of the vector layer is processed by a data editing and processing tool to obtain the target vector layer, which specifically includes S201-S204: S201, simplifying the vector of the vector layer to obtain a target vector.

[0042] In one example, the LStarGIS system first analyzes the input vector layer, including basic information such as its geometry type (point, line, surface), number of vertices, spatial range, etc. Based on this information, the system automatically determines whether simplification is needed and recommends appropriate simplification algorithms and parameters. For point features, simplification is usually not required; for line and surface features, the system provides a variety of simplification algorithms to choose from, the most commonly used of which is the Douglas-Peucker algorithm.

[0043] Before performing the simplification operation, the system prompts the user to set a simplification threshold parameter. This parameter is usually expressed in map units (such as meters) or pixels, and it determines the degree of simplification. A smaller threshold retains more details, while a larger threshold produces simpler shapes. The system recommends an initial threshold based on the characteristics of the data and the expected usage scenario (such as map scale), and the user can adjust it as needed.

[0044] Once the simplification process begins, the LStarGIS system processes each feature in the vector layer one by one. For line features, the system uses a selected algorithm (such as the Douglas-Peucker algorithm) to reduce the number of vertices that make up the line. The algorithm recursively splits the original line segment into simpler parts and removes those intermediate points whose deviation from the original line segment is less than a threshold. For surface features, the system processes its outer boundary first, and then processes the internal holes (if any). During the processing, the system pays special attention to maintaining topological relationships, ensuring that the simplified polygons do not intersect with themselves and that there are no overlaps or gaps between adjacent polygons.

[0045] In order to ensure the quality of the simplified results, the system will perform a series of checks and corrections during the simplification process. For example, for surface features, the system will ensure that the area change after simplification does not exceed the preset percentage; for line features, the system will ensure that the simplified line segments will not accidentally intersect with other features. If potential problems are found, the system will automatically adjust the simplification parameters or locally retain more vertices.

[0046] After simplification, the LStarGIS system will generate a new vector layer as the target vector. This new layer is consistent with the original layer in terms of attribute structure, but the geometry is simplified. The system will automatically calculate and display the statistical information before and after simplification, such as the percentage of reduction in the number of vertices, the change in file size, etc., so that users can intuitively understand the effect of simplification.

[0047] S202, dividing the target vector into at least two sub-vectors, and adding nodes at the corners of the sub-vectors by using a node tool, where the corners are positions on the sub-vectors where the curvature is greater than a preset curvature threshold.

[0048] In one example, the LStarGIS system first analyzes the geometric features of the target vector, including its shape complexity, length (for line features) or area (for surface features), etc. Based on these features, the system automatically determines the appropriate split method and number. The purpose of splitting is to decompose complex vector features into sub-units that are easier to manage and process, while maintaining the topological relationship and attribute information of the original features.

[0049] For line features, the system may use equal length segmentation or feature point segmentation. Equal length segmentation divides the line feature evenly into several segments with similar lengths; feature point segmentation splits the line feature at key locations (such as sharp turns). For area features, the system may determine the segmentation scheme based on area, shape complexity, or internal features (such as internal boundaries). For example, for a complex polygon, the system may split it along its main axis or natural dividing line.

[0050] After the segmentation is completed, the LStarGIS system will perform independent node addition processing on each sub-vector. This process first involves the calculation of curvature. The system will calculate the curvature value point by point along the boundary of the sub-vector. Curvature is a mathematical quantity that describes the degree of curvature of a curve. It reflects the degree to which the curve deviates from a straight line near a certain point. For discrete vector data, the system usually uses the three-point method to approximate the curvature, that is, considering the current point and its two adjacent points before and after to estimate the curvature.

[0051] The system automatically recommends a curvature threshold based on data characteristics and intended use, and the user can adjust this threshold as needed. A lower threshold will cause more nodes to be added, thus capturing more details; a higher threshold will produce smoother results.

[0052] After calculating the curvature and determining the threshold, the system will traverse along the boundary of the sub-vector and add new nodes where the curvature is greater than the preset threshold. This process is actually a local encryption of the original vector to better describe complex geometric shapes. When adding nodes, the system will pay special attention to maintaining the integrity of the topological relationship to ensure that the newly added nodes will not cause self-intersection or conflict with adjacent elements.

[0053] S203, performing node editing on the sub-vector after adding the node to obtain a target sub-vector.

[0054] In one example, the LStarGIS system first conducts a comprehensive analysis of each sub-vector after adding nodes, including geometric features such as node distribution density, line segment length changes, angle changes, etc. Based on these features, the system automatically identifies areas that may need further editing, such as areas where nodes are too dense, locations with unnatural sharp turns, or places where there are potential conflicts with adjacent features. This intelligent identification can help users quickly locate areas that need to be focused on and improve editing efficiency.

[0055] The specific operations of node editing include moving nodes, deleting redundant nodes, adding necessary nodes, smoothing nodes, etc. The LStarGIS system provides a complete set of node editing tools, which users can choose to use as needed. For moving nodes, the system supports precise positioning, and users can ensure the accuracy of node positions by entering coordinates or using snap tools. When moving nodes, the system will display the length and angle changes of adjacent line segments in real time to help users make editing decisions.

[0056] Deleting redundant nodes is an important step in optimizing vector data. LStarGIS system provides intelligent node simplification tools, which can automatically identify and delete nodes that do not contribute much to the overall shape. This process takes into account factors such as the spatial distribution of nodes and the angle changes of line segments to ensure that simplification does not significantly change the shape of the feature. Users can set the simplification tolerance, and the system will automatically delete nodes based on this tolerance while retaining key shape features.

[0057] Necessary nodes are added to better describe complex geographic features. In some cases, the automatically added nodes may not be enough to accurately express certain details. The LStarGIS system allows users to add nodes manually, or use intelligent tools to automatically add nodes under certain conditions. For example, the system can automatically add nodes at the midpoint of a curve segment to better approximate the curve shape.

[0058] Smoothing nodes is an important means to improve the visual quality of vector data. LStarGIS system provides a variety of smoothing algorithms, such as B-spline curves, Bezier curves, etc. Users can select an appropriate algorithm to smooth the selected node sequence. The smoothing process will reduce jagged edges while maintaining the overall shape, making the lines smoother and more natural. The system also allows users to control the degree of smoothing to strike a balance between retaining details and obtaining a smooth effect.

[0059] During the node editing process, the LStarGIS system will perform real-time topology checks to ensure that the editing operation will not cause topological errors, such as self-intersection or overlap with adjacent features. If a potential topological problem is detected, the system will immediately remind the user and provide correction suggestions. This real-time checking mechanism greatly reduces the risk of generating erroneous data and improves the efficiency and accuracy of editing.

[0060] S204, merging all target sub-vectors to obtain a target vector layer.

[0061] S103, obtaining an intersection area obtained after performing an intersection operation on multiple target vector layers, and taking the intersection area as a first result layer.

[0062] In one example, the system first needs to identify the layers involved in the intersection operation. These layers may come from the target vector layer that has been edited in the previous step, or they may be other related layers newly imported by the user. For example, in an urban planning project, it may be necessary to perform intersection operations on the land use status layer, planning control line layer, and ecological protection area layer. The system will require the user to select the layers involved in the intersection operation through the graphical interface and specify the priority of the operation.

[0063] Next, the LStarGIS system will call the built-in spatial analysis module to perform intersection operations. This process is actually a complex geometric operation. The system will compare the geometric elements in the selected layer pair by pair to determine whether they intersect in space. In order to improve the efficiency of the operation, the system first uses spatial indexing technology (such as R-tree) to quickly screen the possible intersecting feature pairs, and then performs detailed geometric intersection judgments on these candidate feature pairs. In the judgment process, the system will consider the intersection between different types of geometric elements (points, lines, and surfaces), such as the inclusion relationship between points and surfaces, the intersection relationship between lines and surfaces, etc.

[0064] The result of the intersection operation is a new vector layer, the first result layer. This layer contains the areas where all input layers overlap in space. For each overlapping area, the system not only retains its geometry, but also inherits the attribute information of all the original layers involved in the intersection. For example, if an area belongs to both "commercial land" and "ecological protection area", then in the result layer, this area will contain the "commercial land" attribute from the land use layer and the relevant attributes from the ecological protection area layer.

[0065] To ensure the quality and usability of the first result layer, the system will perform a series of post-processing after the intersection operation is completed. This includes repairing possible geometric errors (such as self-intersecting polygons, overlapping nodes, etc.), merging adjacent polygons with the same attributes, and recalculating basic statistics such as area and perimeter. The system will also automatically generate a new attribute field to identify the original layer combination corresponding to each result feature, which is helpful for subsequent classification and analysis.

[0066] After using the intersection area as the first result layer, it also includes: A topological rule check is performed on the first result layer, and the topological errors found are marked on the first result layer. The topological rules include no overlapping patches and no gaps in patches. The topological errors marked on the first result layer are corrected using editing tools to ensure that the first result layer has no topological errors.

[0067] In one example, the LStarGIS system first applies predefined topological rules to the first result layer, including no overlapping patches and no gaps. These two rules are basic requirements for ensuring the integrity of surface feature data. The no overlapping patch rule requires that there should be no overlapping areas between adjacent polygons, while the no gap rule requires that there should be no gaps between polygons. The application of these rules ensures the logical continuity and integrity of spatial data, which is crucial for many spatial analysis and mapping tasks.

[0068] The system uses efficient spatial indexing and geometric algorithms to perform topology checks. For the patch non-overlap rule, the system checks the boundaries of each pair of adjacent polygons, calculates their intersection, and marks an overlap error if there is an intersection with a non-zero area. For the patch seamless rule, the system analyzes the boundaries between adjacent polygons and marks a gap error if a gap between the boundaries is detected (i.e., it does not fit perfectly).

[0069] During the inspection process, the LStarGIS system will generate an error report in real time and graphically identify the detected topological errors on the first result layer. This visual error identification method allows users to intuitively understand the location and type of errors, greatly improving the efficiency of subsequent correction work. The system usually uses different colors or symbols to distinguish different types of topological errors. For example, red may be used to indicate overlap errors and yellow to indicate gap errors.

[0070] After completing the detection and identification of topological errors, the LStarGIS system provides a set of powerful editing tools to correct these errors. These tools include but are not limited to: automatic repair tools, manual editing tools, and advanced topological editing tools. Automatic repair tools can handle some simple topological errors, such as small overlaps or narrow gaps. For more complex situations, the system provides manual editing tools that allow users to accurately adjust the boundaries of polygons.

[0071] During the correction process, the system will update the topological relationship in real time to ensure that each editing operation will not introduce new topological errors. For example, when the user moves the boundary of a polygon to eliminate overlap, the system will automatically adjust the boundaries of adjacent polygons to maintain boundary consistency. This intelligent editing mechanism greatly reduces the risk of introducing new errors manually.

[0072] Based on the above embodiment, as an optional implementation, in S103, obtaining the intersection area obtained after performing intersection operation on multiple target vector layers, and taking the intersection area as the first result layer specifically includes S301-S303: S301, select multiple target vector layers, and set the parameters of the intersection operation, the parameters including the path and name of the input layer and the output layer.

[0073] In one example, the LStarGIS system first provides an intuitive user interface that allows users to browse and select the vector layers available in the system. This interface usually includes a layer list that displays basic information such as the name, type (point, line, surface), coordinate system, etc. of each layer. Users can select multiple target vector layers by checking boxes or dragging. The system will display the number of selected layers in real time and highlight these layers in the map window, allowing users to intuitively confirm whether the selection is correct.

[0074] During the layer selection process, the LStarGIS system will perform a series of compatibility checks. For example, the system will verify whether the coordinate system of the selected layer is consistent. If there is an inconsistency, the system will remind the user and provide the option of coordinate conversion. In addition, the system will check whether the geometry type of the layer is suitable for intersection operations. For example, at least one polygon layer must be present to perform face-to-face intersection. These checks help avoid possible errors in subsequent processing and improve the reliability of the entire analysis process.

[0075] After selecting the input layer, the user needs to set the parameters of the intersection operation. The first step is to specify the path and name of the output layer. The LStarGIS system will provide a file dialog box for the user to select the save location and input file name of the output file. The system will automatically check the validity of the file name to ensure that it complies with the naming rules of the operating system and avoid overwriting existing files. The user can also select the format of the output file, such as Shapefile, GeoJSON, etc. The system will automatically add the correct file extension according to the selected format.

[0076] S302: Perform overlay analysis on multiple target vector layers according to the parameters to obtain geometric intersection areas between the target vector layers.

[0077] In one example, the LStarGIS system will first load the selected target vector layer according to the parameters set by the user in step S301. The system will verify the consistency of the coordinate systems of all layers. If inconsistencies are found, the coordinates will be automatically converted to ensure that all layers are analyzed in the same spatial reference system. This step is a key prerequisite for ensuring the accuracy of the intersection results.

[0078] Next, the system will select the most suitable intersection algorithm based on the layer geometry type and data volume. For a simple intersection of two polygon layers, the system may use a traditional plane scanline algorithm. For complex multi-layer intersections or large data volumes, the system may choose a more efficient algorithm, such as an R-tree-based spatial index algorithm or a parallel processing algorithm. This intelligent algorithm selection can significantly improve the efficiency of intersection operations, especially when processing large-scale spatial data.

[0079] When performing intersection operations, the LStarGIS system uses a high-precision geometric operation library to ensure the accuracy of the intersection results. The system processes each polygon one by one and calculates its intersection with polygons in other layers. In this process, the system takes into account the spatial tolerance set by the user to handle minor geometric errors that may be caused by numerical precision limitations. This processing method can effectively avoid false intersections or missed intersections caused by inconsistent data precision.

[0080] For the intersection of multiple layers, the LStarGIS system will adopt an optimized processing strategy. For example, the system may first intersect the layer pairs with more overlapping spatial ranges, and then gradually incorporate other layers. This strategy can reduce the complexity of intermediate results and improve overall computing efficiency. In addition, the system will also use parallel computing technology to split large data sets into multiple subsets and perform calculations on multiple processing cores at the same time, thereby speeding up processing.

[0081] During the intersection process, the LStarGIS system will not only process geometric information, but also attribute information according to the user's settings. The system can retain all attribute fields of the original layer, or only retain some key fields as specified by the user. For the new polygons generated by the intersection, the system will combine the attributes from different input layers according to preset rules. For example, you can choose to retain the attributes of the original polygon with the largest area, or create a new attribute field to store the intersection information.

[0082] In order to improve the efficiency of large-scale data processing, the LStarGIS system adopts a block processing strategy. The system divides the entire study area into multiple smaller spatial blocks, performs intersection operations on the data in each block, and then merges the results. This method can not only make full use of computing resources, but also effectively control memory usage, enabling the system to process ultra-large-scale spatial data sets.

[0083] During the intersection operation, the LStarGIS system will display progress information in real time, including the number of layers processed, the number of generated intersection polygons, the estimated remaining time, etc. This real-time feedback not only allows users to understand the processing progress, but also helps to detect potential problems in a timely manner. If the system detects an abnormal situation, such as the intersection operation of a certain area is particularly time-consuming, the user will be notified immediately, allowing the user to choose whether to continue processing or adjust parameters.

[0084] After the intersection operation is completed, the LStarGIS system will generate a new vector layer containing the geometric and attribute information of all intersecting areas. The system will perform a preliminary quality check on this new layer to verify the geometric validity, attribute integrity, etc. If any problems are found, the system will generate a detailed error report to help users quickly locate and solve the problem.

[0085] S303, converting the geometric intersection area into a new vector layer as the first result layer.

[0086] S104: Classify and render the first result layer according to the layer attribute of the first result layer to obtain a second result layer.

[0087] In one example, the LStarGIS system will first analyze the attribute table structure of the first result layer. This layer contains the comprehensive information obtained after the intersection operation, and its attribute fields may come from multiple original layers, so the information is often complex. The system will automatically identify key attribute fields, such as land attributes, administrative divisions, environmental sensitivity, etc., and provide users with options as the basis for classification rendering. At the same time, the system will also analyze the data types (such as text type, numeric type) and data distribution characteristics of these fields to provide suggestions for subsequent classification methods.

[0088] Next, the system will provide several commonly used classification methods for users to choose from based on the selected attribute fields and data characteristics. For categorical data (such as land use type), the system will use the unique value classification method by default, assigning a unique color or symbol to each category. For continuous numerical data (such as population density), the system will provide equal interval classification, natural breakpoint classification, quantile classification and other methods. Users can choose the appropriate classification method based on data characteristics and analysis purposes, and can adjust the number of categories. For example, when analyzing the urban heat island effect, the surface temperature field may be selected and divided into 5 levels using the natural breakpoint method.

[0089] Once the classification method is determined, the LStarGIS system will automatically assign a color or symbol to each category. The system has built-in color schemes, including single-color gradients, multi-color gradients, contrasting colors, etc. These schemes have been carefully designed to take into account both visual aesthetics and professional map-making standards. For example, for a classification representing temperature, the system may default to a gradient color scheme from blue (cold) to red (hot). Users can adjust the color scheme as needed, and the system also provides the option of customizing colors to meet special mapping needs.

[0090] In addition to color rendering, the system also supports adjusting other visual variables of features according to attribute values, such as transparency, symbol size, etc. This multi-dimensional rendering capability allows the map to express multiple attribute information at the same time. For example, when analyzing the development potential of a city, color can be used to represent land use type, while transparency can be used to represent development intensity.

[0091] After rendering is completed, the system will generate a second result layer. This layer is exactly the same as the first result layer in terms of geometry and attribute structure, but with additional rendering information. The system will automatically generate a legend to explain the meaning of various colors and symbols, and allow users to edit the legend title and description.

[0092] Based on the above embodiment, as an optional implementation, in S104, according to the layer attribute of the first result layer, the first result layer is classified and rendered to obtain the second result layer, which specifically includes: Extract rendering properties of multiple spot elements in the first result layer; match rendering symbol parameters corresponding to each spot element in the rendering rule library according to the rendering properties; perform symbolic rendering on each spot element according to the rendering symbol parameters corresponding to each spot element to generate rendered spot elements; construct all rendered spot elements into a result layer after symbolic rendering to obtain a second result layer.

[0093] In one example, the LStarGIS system first extracts the rendering attributes of each tile feature from the first result layer. These attributes may include key information such as the type, area, and theme value of the tile. The system uses efficient data access methods, such as index queries or parallel processing, to quickly traverse the entire layer and extract the required attribute information. This step is the basis for the subsequent rendering process, because these attributes will determine how to select appropriate rendering symbols for each tile.

[0094] Next, the system will match the rendering symbol parameters corresponding to each patch feature in the pre-defined rendering rule library based on the extracted rendering attributes. The rendering rule library is a database containing various rendering styles and parameters, which defines how spatial features of different types or attributes should be presented on the map. The LStarGIS system uses an intelligent matching algorithm to consider multiple factors such as patch type, attribute value range, spatial relationship, etc., to select the most appropriate rendering symbol for each patch. This process not only considers the characteristics of a single patch, but also the visual harmony of the overall map, ensuring that the final rendering result can accurately express the data information while maintaining good readability and aesthetics.

[0095] During the matching process, the LStarGIS system also considers the custom rendering rules that users may set. The system provides a flexible interface that allows users to modify or create rendering rules according to specific needs. For example, users can define unique color schemes for specific types of patches, or set gradient fill styles based on attribute values. This flexibility allows the system to adapt to the special needs of various professional fields, thereby generating more customized and professional map representations.

[0096] Once the rendering symbol parameters of each patch feature are determined, the LStarGIS system will begin the actual symbol rendering process. The system uses a high-performance graphics rendering engine that can quickly process a large number of patch features. The rendering process includes filling colors, drawing boundary lines, adding textures or patterns, etc. The system will accurately control the visual performance of each patch according to the rendering symbol parameters, including color, transparency, line type, fill style, etc. In order to improve rendering efficiency, the system uses multi-threaded processing and GPU acceleration technology, which can handle the rendering tasks of multiple patches in parallel, significantly improving the overall rendering speed.

[0097] S105, performing map finishing on the second result layer to obtain a target map file.

[0098] In one example, the LStarGIS system first enters the map making mode and places the second result layer in the drawing canvas. The system automatically adjusts the display range of the map to ensure that the study area is fully presented on the canvas. Then, the system automatically calculates and adds the scale and compass according to the canvas size and map scale. These two elements are crucial for readers to understand the spatial scale and direction of the map. The system provides a variety of styles of scales and compasses to choose from, and users can adjust them according to the map theme and personal preferences.

[0099] The system will then automatically generate a legend based on the rendering information of the second result layer. The layout and style of the legend will be automatically adjusted based on the map theme and rendering method to ensure clarity and readability. For example, for thematic maps with layered colors, the system will generate a color-graded legend; for point features expressed using symbols, the system will generate corresponding symbol descriptions. Users can further edit the legend title, adjust the legend position and size to optimize the legend's performance.

[0100] Next, the LStarGIS system will prompt the user to add a map title. The map title is the core of the entire map, and it succinctly summarizes the theme and content of the map. The system provides a variety of font and style options, and intelligently recommends the appropriate font size and position based on the map theme. Users can adjust the content and style of the title as needed to ensure that it can accurately convey the main information of the map.

[0101] In order to increase the information content and readability of the map, the system also supports adding auxiliary text information, such as map description, data source, mapping date, etc. This information is usually placed on the edge or blank space of the map, and the system will intelligently suggest a suitable location to avoid blocking important map elements.

[0102] During the finishing process, LStarGIS system also provides the function of adding additional layers, such as administrative boundaries, main roads, water systems and other background information. These auxiliary layers can help readers better understand the spatial background of map content. The system will automatically adjust the display style of these layers.

[0103] Finally, the system will optimize the overall layout of the entire map. This includes adjusting the spatial relationship between elements to ensure visual balance; adding appropriate white space to enhance the overall aesthetics of the map; and adding borders and grid lines as needed to improve the standardization of the map. The system also provides a variety of preset map templates, which users can quickly apply and then make personalized adjustments.

[0104] After finishing, LStarGIS system will generate the target map file. This file not only contains the visualized map image, but also retains all the geospatial information and attribute data. The system supports multiple output formats, including common image formats (such as PNG, JPEG), vector formats (such as PDF, SVG) and professional geographic information formats (such as GeoPDF). Users can choose the appropriate output format according to subsequent use requirements.

[0105] The second result layer is subjected to map finishing to obtain the target map file, including: The second result layer is symbolized according to preset mapping rules to obtain a symbolized result layer; on the symbolized result layer, labeling information of the layer elements is automatically generated according to preset labeling rules to obtain a third result layer; the layout of the third result layer is optimized, and preset necessary map elements are added to obtain a fourth result layer; the fourth result layer is exported to a target map file, and the format of the target map file is a general electronic map format of vector or raster.

[0106] In one example, the LStarGIS system further symbolizes the second result layer according to preset mapping rules. The system automatically adjusts the size, color, and style of symbols according to different map scales and theme types. For example, in small-scale maps, the system may simplify certain complex symbols to avoid visual confusion; in thematic maps, the system will highlight the elements related to the theme and weaken other background information. The LStarGIS system uses an intelligent symbolization algorithm that can automatically handle symbol conflicts, such as adjusting the position of overlapping symbols or merging similar adjacent symbols to ensure the clarity of the map. In addition, the system will also consider factors such as color harmony and visual hierarchy to create a map presentation that is both beautiful and effective.

[0107] Next, the LStarGIS system will automatically generate annotation information for the layer elements on the symbolized result layer according to the preset annotation rules. The system uses advanced automatic annotation algorithms to intelligently generate annotations for various spatial elements, including point, line, and surface elements. During the annotation process, the system considers multiple factors, such as the position, direction, size, and font of the annotation. In order to avoid overlapping and confusion of annotations, the system uses complex conflict detection and resolution algorithms to automatically adjust the annotation position or omit secondary annotations when necessary. For linear elements (such as rivers and roads), the system can generate curved annotations along the direction of the elements; for surface elements, the system will select the best annotation position based on the area size and shape. In addition, the system also supports multi-language annotations and dynamic annotations, which can dynamically display or hide annotations according to the map zoom level. The result of this process is a third result layer, which adds rich annotation information on the basis of symbolization, further improving the information capacity and readability of the map.

[0108] After obtaining the third result layer, the LStarGIS system will optimize the layout and add preset essential map elements. The system will automatically adjust the main position of the map to ensure that important areas are properly displayed. At the same time, the system will add various essential elements to the map, such as scale, compass, legend, latitude and longitude grid, map information, etc. The position and style of these elements will be automatically adjusted according to the main content and overall layout of the map to achieve the best visual effect. For thematic maps, the system will also add corresponding thematic legends and explanatory text. In addition, the LStarGIS system supports custom map templates. Users can design map layouts according to specific needs, and the system will automatically complete the layout according to the template. The result of this process is the fourth result layer, which is a complete and professional map product that contains all the necessary map elements and information.

[0109] Finally, the LStarGIS system will export the fourth result layer as a target map file. The system supports a variety of common electronic map formats, including vector formats (such as SVG, PDF, AI) and raster formats (such as PNG, TIFF, JPEG). During the export process, the system will perform corresponding optimizations based on the selected format. For vector formats, the system will retain the editability of map elements to ensure that they can still be modified in subsequent use; for raster formats, the system will generate high-quality image files based on preset resolution and compression parameters. During the export process, the system will also perform a final quality check to ensure that all map elements are displayed correctly without missing or errors.

[0110] Before obtaining vector geographic information data in various formats, it also includes: Receive data acquisition instructions input by the user, the data acquisition instructions include location information of the data source; according to the location information, obtain vector geographic information data in the data source through file import or network data interface; perform coordinate conversion and data format conversion on the acquired vector geographic information data, convert the acquired vector geographic information data into a data format compatible with the desktop geographic information system and vector geographic information data under a unified coordinate system, and use the converted vector geographic information data as vector geographic information data in multiple formats.

[0111] In one example, the LStarGIS system receives a data acquisition instruction input by the user. This instruction contains the location information of the data source, which may be a local file path, a network URL, or a database connection string. The system provides an intuitive user interface that allows users to easily specify the location of the data source. For frequently used data sources, the system also supports saving and quickly calling data source configurations to improve user operation efficiency. This flexible data acquisition mechanism enables the LStarGIS system to adapt to various data acquisition scenarios, whether it is acquiring data from a local file system, a remote server, or an online geographic data service.

[0112] After receiving the data acquisition instruction, the LStarGIS system will automatically select the appropriate data acquisition method based on the location information. For local files, the system will directly read the file content; for network data, the system will access the remote data source through HTTP, FTP or proprietary protocols. The LStarGIS system supports a variety of common geographic data formats, such as Shapefile, GeoJSON, KML, etc., as well as various spatial database formats. The system uses an efficient data reading algorithm that can quickly process large amounts of data. For large data sets, the system also supports block reading and streaming processing to effectively reduce memory usage. In addition, the LStarGIS system also implements a data caching mechanism that can cache frequently accessed remote data and improve the response speed of subsequent operations.

[0113] After successfully acquiring the data, the LStarGIS system will perform key coordinate conversion and data format conversion steps. The purpose of this step is to unify data from various sources into a standard format and coordinate system within the system to ensure data consistency and comparability. During the coordinate conversion process, the system automatically identifies the coordinate system of the source data and converts it to a predefined unified coordinate system (such as WGS84 or a local coordinate system). The LStarGIS system has a rich built-in coordinate system definition library and conversion parameters, which can handle various coordinate systems around the world. The system also supports custom coordinate systems and conversion parameters to meet the needs of special projects.

[0114] Based on the above method, the present application also discloses a geographic information processing device, such as Figure 2As shown, Figure 2 is a schematic diagram of the structure of a geographic information processing device provided in an embodiment of the present application, the device comprises: a first acquisition module, an editing module, a second acquisition module, a rendering module and an output module; wherein, The first acquisition module is used to acquire vector geographic information data in various formats, and the vector geographic information data includes at least one vector layer containing land attributes; the editing module is used to process the vectors of the vector layer through a data editing and processing tool to obtain a target vector layer; the second acquisition module is used to acquire the intersection area obtained after performing an intersection operation on multiple target vector layers, and use the intersection area as the first result layer; the rendering module is used to classify and render the first result layer according to the layer attributes of the first result layer to obtain a second result layer; the output module is used to perform map finishing on the second result layer to obtain a target map file.

[0115] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0116] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0117] The communication bus 1002 is used to realize the connection and communication between these components.

[0118] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0119] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0120] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0121] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a geographic information processing method.

[0122] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program storing a geographic information processing method in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0123] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0124] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0125] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0130] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A geographic information processing method, characterized in that: Applied to a desktop geographic information system, the method comprises: Acquire vector geographic information data in multiple formats, wherein the vector geographic information data includes at least one vector layer including land attributes; Processing the vectors of the vector layer by using a data editing and processing tool to obtain a target vector layer; Obtaining an intersection area obtained by performing an intersection operation on the plurality of target vector layers, and using the intersection area as a first result layer; According to the layer attribute of the first result layer, the first result layer is classified and rendered to obtain a second result layer; The second result layer is subjected to map finishing to obtain a target map file.

2. The geographic information processing method according to claim 1, characterized in that: The step of processing the vectors of the vector layer by a data editing and processing tool to obtain a target vector layer includes: Simplifying the vector of the vector layer to obtain a target vector; The target vector is divided into at least two sub-vectors, and nodes are added at the corners of the sub-vectors by using a node tool, wherein the corners are positions on the sub-vectors where the curvature is greater than a preset curvature threshold; Performing node editing on the sub-vector after adding the node to obtain a target sub-vector; All the target sub-vectors are combined to obtain a target vector layer.

3. The geographic information processing method according to claim 1, characterized in that: After taking the intersection area as the first result layer, the method further includes: Performing a topology rule check on the first result layer, and marking the topology errors found on the first result layer, wherein the topology rule includes no overlapping of patches and no gaps of patches; The topological error identified on the first result layer is corrected using an editing tool so that the first result layer is free of the topological error.

4. The geographic information processing method according to claim 1, characterized in that: The step of obtaining an intersection area obtained by performing an intersection operation on the plurality of target vector layers and taking the intersection area as a first result layer includes: Select multiple target vector layers, and set parameters for intersection operation, wherein the parameters include paths and names of input layers and output layers; According to the parameters, the multiple target vector layers are overlaid and analyzed to obtain geometric intersection areas between the target vector layers; The geometric intersection area is converted into a new vector layer as the first result layer.

5. The geographic information processing method according to claim 1, characterized in that: The classifying and rendering the first result layer according to the layer attribute of the first result layer to obtain the second result layer includes: Extracting rendering attributes of a plurality of image spot elements in the first result layer; According to the rendering attributes, matching rendering symbol parameters corresponding to each of the image spot elements in a rendering rule library; According to the rendering symbol parameters corresponding to each of the pattern elements, symbolic rendering is performed on each of the pattern elements to generate rendered pattern elements; All the rendered image patch elements are constructed into a symbolically rendered result layer to obtain a second result layer.

6. The geographic information processing method according to claim 1, characterized in that: The step of performing map finishing on the second result layer to obtain a target map file includes: According to a preset mapping rule, the second result layer is symbolized to obtain a symbolized result layer; On the symbolized result layer, automatically generating annotation information of the layer elements according to a preset annotation rule to obtain a third result layer; Optimizing the layout of the third result layer and adding preset necessary map elements to obtain a fourth result layer; The fourth result layer is exported as a target map file, wherein the format of the target map file is a general electronic map format of vector or raster.

7. The geographic information processing method according to claim 1, characterized in that: Before obtaining vector geographic information data in multiple formats, the method further includes: Receiving a data acquisition instruction input by a user, wherein the data acquisition instruction includes location information of a data source; According to the location information, the vector geographic information data in the data source is obtained through file import or network data interface; The acquired vector geographic information data is subjected to coordinate conversion and data format conversion, and the acquired vector geographic information data is converted into a data format compatible with the desktop geographic information system and vector geographic information data under a unified coordinate system, and the converted vector geographic information data is used as the vector geographic information data in the multiple formats.

8. A geographic information processing device, characterized in that: The device comprises: a first acquisition module, an editing module, a second acquisition module, a rendering module and an output module; wherein, The first acquisition module is used to acquire vector geographic information data in multiple formats, wherein the vector geographic information data includes at least one vector layer including land attributes; The editing module is used to process the vectors of the vector layer through a data editing and processing tool to obtain a target vector layer; The second acquisition module is used to obtain the intersection area obtained after performing an intersection operation on the plurality of target vector layers, and use the intersection area as the first result layer; The rendering module is used to classify and render the first result layer according to the layer attribute of the first result layer to obtain a second result layer; The output module is used to perform map finishing on the second result layer to obtain a target map file.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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